PART - A

1. Import and Understand the data [7 Marks]

A. Import and read ‘images.npy’. [1 Marks]

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import cv2
import os
from tensorflow.keras.applications.mobilenet import preprocess_input
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.layers import Reshape, UpSampling2D, Concatenate, Conv2D
from tensorflow.keras.models import Model
from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.metrics import precision_recall_curve,accuracy_score,f1_score,precision_score,recall_score

images_arr = np.load('images.npy',allow_pickle=True)
images_arr.shape

(409, 2)

Loaded the images to images_arr. It has 409 images with image details.

B. Split the data into Features(X) & labels(Y). Unify shape of all the images. [3 Marks]

Imp Note: Replace all the pixels within masked area with 1. Hint: X will comprise of array of image whereas Y will comprise of coordinates of the mask(human face). Observe: data[0], data[0][0], data[0][1].

# To understand the data that is read from images.npy
images_arr[0]

array([array([[[42, 37, 34],
               [56, 51, 48],
               [71, 66, 63],
               ...,
               [23, 33, 34],
               [26, 36, 37],
               [28, 38, 39]],

              [[40, 35, 32],
               [51, 46, 43],
               [64, 59, 56],
               ...,
               [27, 36, 35],
               [24, 33, 32],
               [26, 35, 34]],

              [[43, 38, 35],
               [51, 46, 43],
               [61, 56, 53],
               ...,
               [28, 30, 27],
               [33, 35, 32],
               [35, 37, 34]],

              ...,

              [[56, 47, 40],
               [57, 48, 41],
               [61, 52, 45],
               ...,
               [67, 48, 42],
               [55, 35, 28],
               [60, 40, 33]],

              [[53, 44, 37],
               [54, 45, 38],
               [57, 48, 41],
               ...,
               [59, 40, 34],
               [60, 40, 33],
               [54, 34, 27]],

              [[53, 44, 37],
               [54, 45, 38],
               [57, 48, 41],
               ...,
               [59, 40, 34],
               [70, 50, 43],
               [64, 44, 37]]], dtype=uint8),
       list([{'label': ['Face'], 'notes': '', 'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063}, {'x': 0.1723076923076923, 'y': 0.45345345345345345}], 'imageWidth': 650, 'imageHeight': 333}, {'label': ['Face'], 'notes': '', 'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913}, {'x': 0.6584615384615384, 'y': 0.46846846846846846}], 'imageWidth': 650, 'imageHeight': 333}])],
      dtype=object)

To understand the input images array and its content, let us take a sample

images_arr[0][0]

array([[[42, 37, 34],
        [56, 51, 48],
        [71, 66, 63],
        ...,
        [23, 33, 34],
        [26, 36, 37],
        [28, 38, 39]],

       [[40, 35, 32],
        [51, 46, 43],
        [64, 59, 56],
        ...,
        [27, 36, 35],
        [24, 33, 32],
        [26, 35, 34]],

       [[43, 38, 35],
        [51, 46, 43],
        [61, 56, 53],
        ...,
        [28, 30, 27],
        [33, 35, 32],
        [35, 37, 34]],

       ...,

       [[56, 47, 40],
        [57, 48, 41],
        [61, 52, 45],
        ...,
        [67, 48, 42],
        [55, 35, 28],
        [60, 40, 33]],

       [[53, 44, 37],
        [54, 45, 38],
        [57, 48, 41],
        ...,
        [59, 40, 34],
        [60, 40, 33],
        [54, 34, 27]],

       [[53, 44, 37],
        [54, 45, 38],
        [57, 48, 41],
        ...,
        [59, 40, 34],
        [70, 50, 43],
        [64, 44, 37]]], dtype=uint8)

images_arr[0][1]

[{'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063},
   {'x': 0.1723076923076923, 'y': 0.45345345345345345}],
  'imageWidth': 650,
  'imageHeight': 333},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913},
   {'x': 0.6584615384615384, 'y': 0.46846846846846846}],
  'imageWidth': 650,
  'imageHeight': 333}]

From the above prints, images_arr contains images and its corresponding labels. images_arr[0][0] is an image and images_arr[0][1] holds label details. To understand more let us plot some images.

### Function to select n random images and display images
def img_display(arr,n):
    rand = np.random.randint(0, len(arr), n) # Generating n random numbers from total number of images
    print(rand)
    plt.figure(figsize=(20, 20))
    for i,j in enumerate(rand):
        plt.subplot(1, len(rand), i+1)
        plt.imshow(arr[j][0])
        plt.axis('off')
    plt.show()

img_display(images_arr,5)

[ 17 239 180 387  14]

images = images_arr[:,0]
labels = images_arr[:,1]

For every image in images, labels hold a list of dictionaries holding information on the faces identified. I.e if an image contains 1 face, the corresponding labels list have only one dict. If the image has 5 faces, then the corresponding labels have a list of 5 dicts. Let us see through samples.

plt.imshow(images[207])

<matplotlib.image.AxesImage at 0x29b080e1ca0>

labels[207]

[{'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.03333333333333333, 'y': 0.2760942760942761},
   {'x': 0.41875, 'y': 0.797979797979798}],
  'imageWidth': 480,
  'imageHeight': 297}]

Image 207 has only one person and hence its corresponding labels hold a list of 1 dict.

plt.imshow(images[4])

<matplotlib.image.AxesImage at 0x29b08159a60>

labels[4]

[{'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.5586206896551724, 'y': 0.19938650306748465},
   {'x': 0.6603448275862069, 'y': 0.3773006134969325}],
  'imageWidth': 580,
  'imageHeight': 326},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.38620689655172413, 'y': 0.18711656441717792},
   {'x': 0.4896551724137931, 'y': 0.4049079754601227}],
  'imageWidth': 580,
  'imageHeight': 326},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.1724137931034483, 'y': 0.23006134969325154},
   {'x': 0.2775862068965517, 'y': 0.44171779141104295}],
  'imageWidth': 580,
  'imageHeight': 326},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.7155172413793104, 'y': 0.19938650306748465},
   {'x': 0.8448275862068966, 'y': 0.41411042944785276}],
  'imageWidth': 580,
  'imageHeight': 326},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.2982758620689655, 'y': 0.44171779141104295},
   {'x': 0.4189655172413793, 'y': 0.6533742331288344}],
  'imageWidth': 580,
  'imageHeight': 326},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.6362068965517241, 'y': 0.4233128834355828},
   {'x': 0.7362068965517241, 'y': 0.6104294478527608}],
  'imageWidth': 580,
  'imageHeight': 326}]

image 4 contains 6 faces, hence corresponding labels is a list of 6 dicts with details of 6 faces (coordinates, width, height, etc)

# To unify the shape of all images, lets set the needed variables
img_width = 224
img_height = 224
alpha = 1

y = np.zeros((int(images_arr.shape[0]), img_height, img_width)) ## Initializing 
X = np.zeros((int(images_arr.shape[0]), img_height, img_width, 3))
for index in range(images_arr.shape[0]):
    image = images_arr[index][0]
    image = cv2.resize(image, dsize=(img_height, img_width), interpolation=cv2.INTER_CUBIC)
    try:
      image = image[:, :, :3]
    except:
      continue
    X[index] = preprocess_input(np.array(image, dtype=np.float32))
    for i in images_arr[index][1]:
        x1 = int(i["points"][0]['x'] * img_width) ## identify the co-ordinates of the face to mask
        x2 = int(i["points"][1]['x'] * img_width) ## identify the co-ordinates of the face to mask
        y1 = int(i["points"][0]['y'] * img_height) ## identify the co-ordinates of the face to mask
        y2 = int(i["points"][1]['y'] * img_height) ## identify the co-ordinates of the face to mask
        y[index][y1:y2, x1:x2] = 1                ## mask the face area with 1

X.shape

(409, 224, 224, 3)

y.shape

(409, 224, 224)

## lets see a sample X and its y
plt.figure(figsize=(10, 5))
n = np.random.randint(0, X.shape[0])
ax1 = plt.subplot(1,2,1)
ax1.set_title("Actual Image")
ax1.imshow(X[n])
ax2 = plt.subplot(1,2,2)
ax2.set_title("Masked Image")
ax2.imshow(y[n])

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

<matplotlib.image.AxesImage at 0x29b079b6640>

The area where the face is present in the image is identified correctly and masked with Yellow.

C. Split the data into train and test[400:9]. [1 Marks]

# Take first 400 images as train and last 9 images as test
X_train = X[:400]
y_train = y[:400]
X_test = X[400:]
y_test = y[400:]

print("Shape of X_train :{}".format(X_train.shape))
print("Shape of y_train :{}".format(y_train.shape))
print("Shape of X_test  :{}".format(X_test.shape))
print("Shape of y_test  :{}".format(y_test.shape))

Shape of X_train :(400, 224, 224, 3)
Shape of y_train :(400, 224, 224)
Shape of X_test  :(9, 224, 224, 3)
Shape of y_test  :(9, 224, 224)

D. Select random image from the train data and display original image and masked image. [2 Marks]

## lets see a sample image from train data and its masked image
plt.figure(figsize=(10, 5))
n = np.random.randint(0, X_train.shape[0])
ax1 = plt.subplot(1,2,1)
ax1.set_title("Actual Image")
ax1.imshow(X_train[n])
ax2 = plt.subplot(1,2,2)
ax2.set_title("Masked Image")
ax2.imshow(y_train[n])

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

<matplotlib.image.AxesImage at 0x29b07b708e0>

All the faces in the above image are correctly identified and masked with yellow.

2. Model building [11 Marks]

A. Design a face mask detection model. [4 Marks]

Hint: 1. Use MobileNet architecture for initial pre-trained non-trainable layers. Hint: 2. Add appropriate Upsampling layers to imitate U-net architecture.

def create_fmd_model(trainable=True):
    cv_model = MobileNet(input_shape=(img_height, img_width, 3), include_top=False, alpha=1.0, weights="imagenet")

    for layer in cv_model.layers:
        layer.trainable = trainable
  
    block0 = cv_model.get_layer("conv_pw_1_relu").output
    block1 = cv_model.get_layer("conv_pw_3_relu").output
    block2 = cv_model.get_layer("conv_pw_5_relu").output
    block3 = cv_model.get_layer("conv_pw_11_relu").output
    block4 = cv_model.get_layer("conv_pw_13_relu").output

    x = Concatenate()([UpSampling2D()(block4), block3])
    print(x.shape)
    x = Concatenate()([UpSampling2D()(x), block2])
    print(x.shape)
    x = Concatenate()([UpSampling2D()(x), block1])
    print(x.shape)
    x = Concatenate()([UpSampling2D()(x), block0])
    print(x.shape)
    x = UpSampling2D()(x)
    print(x.shape)
    x = Conv2D(1, kernel_size=1, activation="sigmoid")(x)
    x = Reshape((img_height, img_width))(x)
    print(x.shape)

    return Model(inputs=cv_model.input, outputs=x)

fmd_model = create_fmd_model()

(None, 14, 14, 1536)
(None, 28, 28, 1792)
(None, 56, 56, 1920)
(None, 112, 112, 1984)
(None, 224, 224, 1984)
(None, 224, 224)

fmd_model.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                
                                )                                                                 
                                                                                                  
 conv1_bn (BatchNormalization)  (None, 112, 112, 32  128         ['conv1[0][0]']                  
                                )                                                                 
                                                                                                  
 conv1_relu (ReLU)              (None, 112, 112, 32  0           ['conv1_bn[0][0]']               
                                )                                                                 
                                                                                                  
 conv_dw_1 (DepthwiseConv2D)    (None, 112, 112, 32  288         ['conv1_relu[0][0]']             
                                )                                                                 
                                                                                                  
 conv_dw_1_bn (BatchNormalizati  (None, 112, 112, 32  128        ['conv_dw_1[0][0]']              
 on)                            )                                                                 
                                                                                                  
 conv_dw_1_relu (ReLU)          (None, 112, 112, 32  0           ['conv_dw_1_bn[0][0]']           
                                )                                                                 
                                                                                                  
 conv_pw_1 (Conv2D)             (None, 112, 112, 64  2048        ['conv_dw_1_relu[0][0]']         
                                )                                                                 
                                                                                                  
 conv_pw_1_bn (BatchNormalizati  (None, 112, 112, 64  256        ['conv_pw_1[0][0]']              
 on)                            )                                                                 
                                                                                                  
 conv_pw_1_relu (ReLU)          (None, 112, 112, 64  0           ['conv_pw_1_bn[0][0]']           
                                )                                                                 
                                                                                                  
 conv_pad_2 (ZeroPadding2D)     (None, 113, 113, 64  0           ['conv_pw_1_relu[0][0]']         
                                )                                                                 
                                                                                                  
 conv_dw_2 (DepthwiseConv2D)    (None, 56, 56, 64)   576         ['conv_pad_2[0][0]']             
                                                                                                  
 conv_dw_2_bn (BatchNormalizati  (None, 56, 56, 64)  256         ['conv_dw_2[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_2_relu (ReLU)          (None, 56, 56, 64)   0           ['conv_dw_2_bn[0][0]']           
                                                                                                  
 conv_pw_2 (Conv2D)             (None, 56, 56, 128)  8192        ['conv_dw_2_relu[0][0]']         
                                                                                                  
 conv_pw_2_bn (BatchNormalizati  (None, 56, 56, 128)  512        ['conv_pw_2[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_2_relu (ReLU)          (None, 56, 56, 128)  0           ['conv_pw_2_bn[0][0]']           
                                                                                                  
 conv_dw_3 (DepthwiseConv2D)    (None, 56, 56, 128)  1152        ['conv_pw_2_relu[0][0]']         
                                                                                                  
 conv_dw_3_bn (BatchNormalizati  (None, 56, 56, 128)  512        ['conv_dw_3[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_3_relu (ReLU)          (None, 56, 56, 128)  0           ['conv_dw_3_bn[0][0]']           
                                                                                                  
 conv_pw_3 (Conv2D)             (None, 56, 56, 128)  16384       ['conv_dw_3_relu[0][0]']         
                                                                                                  
 conv_pw_3_bn (BatchNormalizati  (None, 56, 56, 128)  512        ['conv_pw_3[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_3_relu (ReLU)          (None, 56, 56, 128)  0           ['conv_pw_3_bn[0][0]']           
                                                                                                  
 conv_pad_4 (ZeroPadding2D)     (None, 57, 57, 128)  0           ['conv_pw_3_relu[0][0]']         
                                                                                                  
 conv_dw_4 (DepthwiseConv2D)    (None, 28, 28, 128)  1152        ['conv_pad_4[0][0]']             
                                                                                                  
 conv_dw_4_bn (BatchNormalizati  (None, 28, 28, 128)  512        ['conv_dw_4[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_4_relu (ReLU)          (None, 28, 28, 128)  0           ['conv_dw_4_bn[0][0]']           
                                                                                                  
 conv_pw_4 (Conv2D)             (None, 28, 28, 256)  32768       ['conv_dw_4_relu[0][0]']         
                                                                                                  
 conv_pw_4_bn (BatchNormalizati  (None, 28, 28, 256)  1024       ['conv_pw_4[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_4_relu (ReLU)          (None, 28, 28, 256)  0           ['conv_pw_4_bn[0][0]']           
                                                                                                  
 conv_dw_5 (DepthwiseConv2D)    (None, 28, 28, 256)  2304        ['conv_pw_4_relu[0][0]']         
                                                                                                  
 conv_dw_5_bn (BatchNormalizati  (None, 28, 28, 256)  1024       ['conv_dw_5[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_5_relu (ReLU)          (None, 28, 28, 256)  0           ['conv_dw_5_bn[0][0]']           
                                                                                                  
 conv_pw_5 (Conv2D)             (None, 28, 28, 256)  65536       ['conv_dw_5_relu[0][0]']         
                                                                                                  
 conv_pw_5_bn (BatchNormalizati  (None, 28, 28, 256)  1024       ['conv_pw_5[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_5_relu (ReLU)          (None, 28, 28, 256)  0           ['conv_pw_5_bn[0][0]']           
                                                                                                  
 conv_pad_6 (ZeroPadding2D)     (None, 29, 29, 256)  0           ['conv_pw_5_relu[0][0]']         
                                                                                                  
 conv_dw_6 (DepthwiseConv2D)    (None, 14, 14, 256)  2304        ['conv_pad_6[0][0]']             
                                                                                                  
 conv_dw_6_bn (BatchNormalizati  (None, 14, 14, 256)  1024       ['conv_dw_6[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_6_relu (ReLU)          (None, 14, 14, 256)  0           ['conv_dw_6_bn[0][0]']           
                                                                                                  
 conv_pw_6 (Conv2D)             (None, 14, 14, 512)  131072      ['conv_dw_6_relu[0][0]']         
                                                                                                  
 conv_pw_6_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_pw_6[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_6_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_pw_6_bn[0][0]']           
                                                                                                  
 conv_dw_7 (DepthwiseConv2D)    (None, 14, 14, 512)  4608        ['conv_pw_6_relu[0][0]']         
                                                                                                  
 conv_dw_7_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_dw_7[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_7_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_dw_7_bn[0][0]']           
                                                                                                  
 conv_pw_7 (Conv2D)             (None, 14, 14, 512)  262144      ['conv_dw_7_relu[0][0]']         
                                                                                                  
 conv_pw_7_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_pw_7[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_7_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_pw_7_bn[0][0]']           
                                                                                                  
 conv_dw_8 (DepthwiseConv2D)    (None, 14, 14, 512)  4608        ['conv_pw_7_relu[0][0]']         
                                                                                                  
 conv_dw_8_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_dw_8[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_8_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_dw_8_bn[0][0]']           
                                                                                                  
 conv_pw_8 (Conv2D)             (None, 14, 14, 512)  262144      ['conv_dw_8_relu[0][0]']         
                                                                                                  
 conv_pw_8_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_pw_8[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_8_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_pw_8_bn[0][0]']           
                                                                                                  
 conv_dw_9 (DepthwiseConv2D)    (None, 14, 14, 512)  4608        ['conv_pw_8_relu[0][0]']         
                                                                                                  
 conv_dw_9_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_dw_9[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_dw_9_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_dw_9_bn[0][0]']           
                                                                                                  
 conv_pw_9 (Conv2D)             (None, 14, 14, 512)  262144      ['conv_dw_9_relu[0][0]']         
                                                                                                  
 conv_pw_9_bn (BatchNormalizati  (None, 14, 14, 512)  2048       ['conv_pw_9[0][0]']              
 on)                                                                                              
                                                                                                  
 conv_pw_9_relu (ReLU)          (None, 14, 14, 512)  0           ['conv_pw_9_bn[0][0]']           
                                                                                                  
 conv_dw_10 (DepthwiseConv2D)   (None, 14, 14, 512)  4608        ['conv_pw_9_relu[0][0]']         
                                                                                                  
 conv_dw_10_bn (BatchNormalizat  (None, 14, 14, 512)  2048       ['conv_dw_10[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_dw_10_relu (ReLU)         (None, 14, 14, 512)  0           ['conv_dw_10_bn[0][0]']          
                                                                                                  
 conv_pw_10 (Conv2D)            (None, 14, 14, 512)  262144      ['conv_dw_10_relu[0][0]']        
                                                                                                  
 conv_pw_10_bn (BatchNormalizat  (None, 14, 14, 512)  2048       ['conv_pw_10[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_pw_10_relu (ReLU)         (None, 14, 14, 512)  0           ['conv_pw_10_bn[0][0]']          
                                                                                                  
 conv_dw_11 (DepthwiseConv2D)   (None, 14, 14, 512)  4608        ['conv_pw_10_relu[0][0]']        
                                                                                                  
 conv_dw_11_bn (BatchNormalizat  (None, 14, 14, 512)  2048       ['conv_dw_11[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_dw_11_relu (ReLU)         (None, 14, 14, 512)  0           ['conv_dw_11_bn[0][0]']          
                                                                                                  
 conv_pw_11 (Conv2D)            (None, 14, 14, 512)  262144      ['conv_dw_11_relu[0][0]']        
                                                                                                  
 conv_pw_11_bn (BatchNormalizat  (None, 14, 14, 512)  2048       ['conv_pw_11[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_pw_11_relu (ReLU)         (None, 14, 14, 512)  0           ['conv_pw_11_bn[0][0]']          
                                                                                                  
 conv_pad_12 (ZeroPadding2D)    (None, 15, 15, 512)  0           ['conv_pw_11_relu[0][0]']        
                                                                                                  
 conv_dw_12 (DepthwiseConv2D)   (None, 7, 7, 512)    4608        ['conv_pad_12[0][0]']            
                                                                                                  
 conv_dw_12_bn (BatchNormalizat  (None, 7, 7, 512)   2048        ['conv_dw_12[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_dw_12_relu (ReLU)         (None, 7, 7, 512)    0           ['conv_dw_12_bn[0][0]']          
                                                                                                  
 conv_pw_12 (Conv2D)            (None, 7, 7, 1024)   524288      ['conv_dw_12_relu[0][0]']        
                                                                                                  
 conv_pw_12_bn (BatchNormalizat  (None, 7, 7, 1024)  4096        ['conv_pw_12[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_pw_12_relu (ReLU)         (None, 7, 7, 1024)   0           ['conv_pw_12_bn[0][0]']          
                                                                                                  
 conv_dw_13 (DepthwiseConv2D)   (None, 7, 7, 1024)   9216        ['conv_pw_12_relu[0][0]']        
                                                                                                  
 conv_dw_13_bn (BatchNormalizat  (None, 7, 7, 1024)  4096        ['conv_dw_13[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_dw_13_relu (ReLU)         (None, 7, 7, 1024)   0           ['conv_dw_13_bn[0][0]']          
                                                                                                  
 conv_pw_13 (Conv2D)            (None, 7, 7, 1024)   1048576     ['conv_dw_13_relu[0][0]']        
                                                                                                  
 conv_pw_13_bn (BatchNormalizat  (None, 7, 7, 1024)  4096        ['conv_pw_13[0][0]']             
 ion)                                                                                             
                                                                                                  
 conv_pw_13_relu (ReLU)         (None, 7, 7, 1024)   0           ['conv_pw_13_bn[0][0]']          
                                                                                                  
 up_sampling2d (UpSampling2D)   (None, 14, 14, 1024  0           ['conv_pw_13_relu[0][0]']        
                                )                                                                 
                                                                                                  
 concatenate (Concatenate)      (None, 14, 14, 1536  0           ['up_sampling2d[0][0]',          
                                )                                 'conv_pw_11_relu[0][0]']        
                                                                                                  
 up_sampling2d_1 (UpSampling2D)  (None, 28, 28, 1536  0          ['concatenate[0][0]']            
                                )                                                                 
                                                                                                  
 concatenate_1 (Concatenate)    (None, 28, 28, 1792  0           ['up_sampling2d_1[0][0]',        
                                )                                 'conv_pw_5_relu[0][0]']         
                                                                                                  
 up_sampling2d_2 (UpSampling2D)  (None, 56, 56, 1792  0          ['concatenate_1[0][0]']          
                                )                                                                 
                                                                                                  
 concatenate_2 (Concatenate)    (None, 56, 56, 1920  0           ['up_sampling2d_2[0][0]',        
                                )                                 'conv_pw_3_relu[0][0]']         
                                                                                                  
 up_sampling2d_3 (UpSampling2D)  (None, 112, 112, 19  0          ['concatenate_2[0][0]']          
                                20)                                                               
                                                                                                  
 concatenate_3 (Concatenate)    (None, 112, 112, 19  0           ['up_sampling2d_3[0][0]',        
                                84)                               'conv_pw_1_relu[0][0]']         
                                                                                                  
 up_sampling2d_4 (UpSampling2D)  (None, 224, 224, 19  0          ['concatenate_3[0][0]']          
                                84)                                                               
                                                                                                  
 conv2d (Conv2D)                (None, 224, 224, 1)  1985        ['up_sampling2d_4[0][0]']        
                                                                                                  
 reshape (Reshape)              (None, 224, 224)     0           ['conv2d[0][0]']                 
                                                                                                  
==================================================================================================
Total params: 3,230,849
Trainable params: 3,208,961
Non-trainable params: 21,888
__________________________________________________________________________________________________

B. Design your own Dice Coefficient and Loss function. [2 Marks]

from tensorflow import reduce_sum
from tensorflow.keras.backend import epsilon

# Defining dice coefficient 
def dice_coefficient(y_true, y_pred):
    numerator = 2 * reduce_sum(y_true * y_pred)
    denominator = reduce_sum(y_true + y_pred)
    return numerator / (denominator + epsilon())

from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.backend import log

# Defining loss function
def loss(y_true, y_pred):
    return binary_crossentropy(y_true, y_pred) - log(dice_coefficient(y_true, y_pred) + epsilon())

C. Train and tune the model as required. [3 Marks]

fmd_model.compile(loss=loss, optimizer='adam', metrics=[dice_coefficient])

from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau

checkpoint = ModelCheckpoint("model-{loss:.2f}.h5", monitor="loss", verbose=1, save_best_only=True,
                             save_weights_only=True, mode="min")
stop = EarlyStopping(monitor="loss", patience=5, mode="min")
reduce_lr = ReduceLROnPlateau(monitor="loss", factor=0.2, patience=5, min_lr=1e-6, verbose=1, mode="min")

fmd_model_fit=fmd_model.fit(X_train, y_train, epochs=10, batch_size=1, callbacks=[checkpoint, reduce_lr, stop],validation_data=(X_test, y_test))

Epoch 1/10
400/400 [==============================] - ETA: 0s - loss: 0.4971 - dice_coefficient: 0.7527
Epoch 1: loss did not improve from 0.33218
400/400 [==============================] - 419s 1s/step - loss: 0.4971 - dice_coefficient: 0.7527 - val_loss: 0.9816 - val_dice_coefficient: 0.5362 - lr: 0.0010
Epoch 2/10
400/400 [==============================] - ETA: 0s - loss: 0.3748 - dice_coefficient: 0.8038
Epoch 2: loss did not improve from 0.33218
400/400 [==============================] - 407s 1s/step - loss: 0.3748 - dice_coefficient: 0.8038 - val_loss: 1.0087 - val_dice_coefficient: 0.5661 - lr: 0.0010
Epoch 3/10
400/400 [==============================] - ETA: 0s - loss: 0.3099 - dice_coefficient: 0.8342
Epoch 3: loss improved from 0.33218 to 0.30993, saving model to model-0.31.h5
400/400 [==============================] - 400s 1s/step - loss: 0.3099 - dice_coefficient: 0.8342 - val_loss: 0.9985 - val_dice_coefficient: 0.5793 - lr: 0.0010
Epoch 4/10
400/400 [==============================] - ETA: 0s - loss: 0.2762 - dice_coefficient: 0.8512
Epoch 4: loss improved from 0.30993 to 0.27623, saving model to model-0.28.h5
400/400 [==============================] - 408s 1s/step - loss: 0.2762 - dice_coefficient: 0.8512 - val_loss: 1.0479 - val_dice_coefficient: 0.5671 - lr: 0.0010
Epoch 5/10
400/400 [==============================] - ETA: 0s - loss: 0.2607 - dice_coefficient: 0.8598
Epoch 5: loss improved from 0.27623 to 0.26068, saving model to model-0.26.h5
400/400 [==============================] - 409s 1s/step - loss: 0.2607 - dice_coefficient: 0.8598 - val_loss: 1.1733 - val_dice_coefficient: 0.5403 - lr: 0.0010
Epoch 6/10
400/400 [==============================] - ETA: 0s - loss: 0.2550 - dice_coefficient: 0.8634
Epoch 6: loss improved from 0.26068 to 0.25496, saving model to model-0.25.h5
400/400 [==============================] - 401s 1s/step - loss: 0.2550 - dice_coefficient: 0.8634 - val_loss: 0.8893 - val_dice_coefficient: 0.5985 - lr: 0.0010
Epoch 7/10
400/400 [==============================] - ETA: 0s - loss: 0.2638 - dice_coefficient: 0.8599
Epoch 7: loss did not improve from 0.25496
400/400 [==============================] - 398s 995ms/step - loss: 0.2638 - dice_coefficient: 0.8599 - val_loss: 1.3310 - val_dice_coefficient: 0.5192 - lr: 0.0010
Epoch 8/10
400/400 [==============================] - ETA: 0s - loss: 0.2825 - dice_coefficient: 0.8513
Epoch 8: loss did not improve from 0.25496
400/400 [==============================] - 396s 990ms/step - loss: 0.2825 - dice_coefficient: 0.8513 - val_loss: 1.1389 - val_dice_coefficient: 0.5374 - lr: 0.0010
Epoch 9/10
400/400 [==============================] - ETA: 0s - loss: 0.2961 - dice_coefficient: 0.8447
Epoch 9: loss did not improve from 0.25496
400/400 [==============================] - 403s 1s/step - loss: 0.2961 - dice_coefficient: 0.8447 - val_loss: 1.1404 - val_dice_coefficient: 0.5253 - lr: 0.0010
Epoch 10/10
400/400 [==============================] - ETA: 0s - loss: 0.3871 - dice_coefficient: 0.8047
Epoch 10: loss did not improve from 0.25496
400/400 [==============================] - 403s 1s/step - loss: 0.3871 - dice_coefficient: 0.8047 - val_loss: 1.1905 - val_dice_coefficient: 0.5229 - lr: 0.0010

D. Evaluate and share insights on performance of the model. [2 Marks]

fmd_model_results = fmd_model.evaluate(X_test, y_test)

1/1 [==============================] - 6s 6s/step - loss: 0.9856 - dice_coefficient: 0.5735

print('Test loss: ', fmd_model_results[0])
print('Test dice coefficient: ', fmd_model_results[1])

Test loss:  0.9856072664260864
Test dice coefficient:  0.5735422372817993

EPOCH=10
dice_coeff_train = fmd_model_fit.history['dice_coefficient']
dice_coeff_val = fmd_model_fit.history['val_dice_coefficient']
epochs = range(1,EPOCH+1)
plt.plot(epochs, dice_coeff_train, 'g', label='Training dice coefficient')
plt.plot(epochs, dice_coeff_val, 'b', label='Validation dice coefficient')
plt.title('Training and Validation dice coefficients')
plt.xlabel('Epochs')
plt.ylabel('Dice coefficient')
plt.legend()
plt.show()

EPOCH=10
loss_train = fmd_model_fit.history['loss']
loss_val = fmd_model_fit.history['val_loss']
epochs = range(1,EPOCH+1)
plt.plot(epochs, loss_train, 'g', label='Training loss')
plt.plot(epochs, loss_val, 'b', label='validation loss')
plt.title('Training and Validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

Insights:

• The dice coefficient seems to fluctuate in the beginning, but at the end seems the fluctuations have decreased.

3. Test the model predictions on the test image: ‘image with index 3 in the test data’ and visualise the predicted masks on the faces in the image. [2 Marks]

# Setting index as 3 and showing the image
idx=3
plt.figure(figsize=(10, 5))

ax1 = plt.subplot(1,2,1)
ax1.set_title("Actual Image")
ax1.imshow(X_test[idx])
ax2 = plt.subplot(1,2,2)
ax2.set_title("Predicted Mask on Image")
ax2.imshow(y_test[idx])

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

<matplotlib.image.AxesImage at 0x29b14557f70>

pred_mask = cv2.resize(1.0 * (fmd_model.predict(x=np.array([X_test[idx]]))[0] > 0.5), (img_width, img_height)) 

1/1 [==============================] - 1s 1s/step

ax1 = plt.subplot(1,2,1)
ax1.set_title("Actual Image")
ax1.imshow(X_test[idx])
ax2 = plt.subplot(1,2,2)
ax2.set_title("Predicted Mask on Image")
ax2.imshow(X_test[idx])
ax2.imshow(pred_mask, alpha=0.9)

#plt.imshow(X_test[idx])
#plt.imshow(pred_mask, alpha=0.9)

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

<matplotlib.image.AxesImage at 0x29b19527c40>

Insights: As shown in the baove images, the model has correctly masked the face on the actual image.

PART B

1. Read/import images from folder ‘training_images’. [2 Marks]

data_folder = 'training_images'
images_list = []

print("Extracting images from training_images folder...")
for img in os.listdir(data_folder):
    image = cv2.imread(os.path.join(data_folder,img)) 
    images_list.append(image)

print("Number of images read : {}".format(len(images_list)))

Extracting images from training_images folder...
Number of images read : 1091

2. Write a loop which will iterate through all the images in the ‘training_images’ folder and detect the faces present on all the images. [3 Marks]

Hint: You can use ’haarcascade_frontalface_default.xml’ from internet to detect faces which is available open source

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'/haarcascade_frontalface_default.xml')
img_df_list=[]
for image in images_list:
    img_df_dict={}
    img_df_dict['orig_img']=image
    temp_image=image.copy()
    gray = cv2.cvtColor(temp_image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.0485258, 6)
    for (x, y, w, h) in faces:
        x1 = x
        x2 = x+w
        y1 = y
        y2 = y+h
        cv2.rectangle(temp_image, (x1,y1), (x2,y2), (255,0,0), 4)    
    img_df_dict['detected_image']=temp_image
    img_df_list.append(img_df_dict)

def show_images(lst,n):
    rand = np.random.randint(0, len(lst), n) # Generating n random numbers from total number of images
    print("Showing images with index in : {}".format(rand))
    plt.figure(figsize=(10, 20))
    for cnt,j in enumerate(rand):
        plt.subplot(n, 2, (cnt*2)+1)
        plt.imshow(lst[j]['orig_img'])
        plt.subplot(n, 2, (cnt*2)+2)
        plt.imshow(lst[j]['detected_image'])

show_images(img_df_list,5)

Showing images with index in : [ 88  78 733 829 816]

3. From the same loop above, extract metadata of the faces and write into a DataFrame. [3 Marks]

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'/haarcascade_frontalface_default.xml')
img_df_list=[]
for image in images_list:
    img_df_dict={}
    img_df_dict['orig_img']=image
    temp_image=image.copy()
    gray = cv2.cvtColor(temp_image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.0485258, 6)
    img_df_dict['metadata'] = []
    for (x, y, w, h) in faces:
        x1 = x
        x2 = x+w
        y1 = y
        y2 = y+h
        point_dict={}
        point_dict['x']=x
        point_dict['y']=y
        point_dict['w']=w
        point_dict['h']=h
        point_dict['coord1']={x1,y1}
        point_dict['coord2']={x2,y2}
                
        img_df_dict['metadata'].append(point_dict)
        cv2.rectangle(temp_image, (x1,y1), (x2,y2), (255,0,0), 4)    
    img_df_dict['detected_image']=temp_image
    img_df_list.append(img_df_dict)

img_df_list[:3]

[{'orig_img': array([[[ 93, 186, 201],
          [ 89, 182, 197],
          [ 82, 177, 192],
          ...,
          [139, 156, 213],
          [136, 153, 210],
          [139, 158, 215]],
  
         [[ 79, 172, 187],
          [ 78, 173, 188],
          [ 79, 174, 189],
          ...,
          [138, 154, 213],
          [134, 150, 209],
          [137, 153, 212]],
  
         [[ 62, 157, 172],
          [ 67, 162, 177],
          [ 74, 171, 185],
          ...,
          [132, 146, 205],
          [127, 143, 202],
          [131, 147, 206]],
  
         ...,
  
         [[107,  75,  99],
          [111,  79, 103],
          [117,  86, 107],
          ...,
          [ 32,  21,  23],
          [ 31,  22,  25],
          [ 32,  23,  26]],
  
         [[105,  76,  99],
          [109,  80, 103],
          [114,  85, 108],
          ...,
          [ 31,  20,  22],
          [ 31,  22,  25],
          [ 33,  24,  27]],
  
         [[109,  80, 103],
          [114,  85, 108],
          [119,  90, 113],
          ...,
          [ 30,  19,  21],
          [ 33,  22,  25],
          [ 33,  24,  27]]], dtype=uint8),
  'metadata': [{'x': 146,
    'y': 139,
    'w': 428,
    'h': 428,
    'coord1': {139, 146},
    'coord2': {567, 574}}],
  'detected_image': array([[[ 93, 186, 201],
          [ 89, 182, 197],
          [ 82, 177, 192],
          ...,
          [139, 156, 213],
          [136, 153, 210],
          [139, 158, 215]],
  
         [[ 79, 172, 187],
          [ 78, 173, 188],
          [ 79, 174, 189],
          ...,
          [138, 154, 213],
          [134, 150, 209],
          [137, 153, 212]],
  
         [[ 62, 157, 172],
          [ 67, 162, 177],
          [ 74, 171, 185],
          ...,
          [132, 146, 205],
          [127, 143, 202],
          [131, 147, 206]],
  
         ...,
  
         [[107,  75,  99],
          [111,  79, 103],
          [117,  86, 107],
          ...,
          [ 32,  21,  23],
          [ 31,  22,  25],
          [ 32,  23,  26]],
  
         [[105,  76,  99],
          [109,  80, 103],
          [114,  85, 108],
          ...,
          [ 31,  20,  22],
          [ 31,  22,  25],
          [ 33,  24,  27]],
  
         [[109,  80, 103],
          [114,  85, 108],
          [119,  90, 113],
          ...,
          [ 30,  19,  21],
          [ 33,  22,  25],
          [ 33,  24,  27]]], dtype=uint8)},
 {'orig_img': array([[[255, 255, 255],
          [255, 255, 255],
          [255, 255, 255],
          ...,
          [255, 255, 255],
          [255, 255, 255],
          [255, 255, 255]],
  
         [[255, 255, 255],
          [255, 255, 255],
          [255, 255, 255],
          ...,
          [255, 255, 255],
          [255, 255, 255],
          [255, 255, 255]],
  
         [[255, 255, 255],
          [255, 255, 255],
          [255, 255, 255],
          ...,
          [255, 255, 255],
          [255, 255, 255],
          [255, 255, 255]],
  
         ...,
  
         [[169, 184, 233],
          [168, 183, 232],
          [167, 183, 230],
          ...,
          [158, 176, 223],
          [157, 175, 222],
          [157, 175, 222]],
  
         [[169, 185, 232],
          [168, 184, 231],
          [167, 183, 230],
          ...,
          [158, 176, 223],
          [157, 174, 223],
          [158, 175, 224]],
  
         [[168, 184, 230],
          [168, 184, 230],
          [168, 184, 230],
          ...,
          [157, 177, 225],
          [157, 177, 225],
          [158, 178, 226]]], dtype=uint8),
  'metadata': [{'x': 80,
    'y': 139,
    'w': 435,
    'h': 435,
    'coord1': {80, 139},
    'coord2': {515, 574}}],
  'detected_image': array([[[255, 255, 255],
          [255, 255, 255],
          [255, 255, 255],
          ...,
          [255, 255, 255],
          [255, 255, 255],
          [255, 255, 255]],
  
         [[255, 255, 255],
          [255, 255, 255],
          [255, 255, 255],
          ...,
          [255, 255, 255],
          [255, 255, 255],
          [255, 255, 255]],
  
         [[255, 255, 255],
          [255, 255, 255],
          [255, 255, 255],
          ...,
          [255, 255, 255],
          [255, 255, 255],
          [255, 255, 255]],
  
         ...,
  
         [[169, 184, 233],
          [168, 183, 232],
          [167, 183, 230],
          ...,
          [158, 176, 223],
          [157, 175, 222],
          [157, 175, 222]],
  
         [[169, 185, 232],
          [168, 184, 231],
          [167, 183, 230],
          ...,
          [158, 176, 223],
          [157, 174, 223],
          [158, 175, 224]],
  
         [[168, 184, 230],
          [168, 184, 230],
          [168, 184, 230],
          ...,
          [157, 177, 225],
          [157, 177, 225],
          [158, 178, 226]]], dtype=uint8)},
 {'orig_img': array([[[ 35,  39,  34],
          [ 37,  41,  36],
          [ 35,  39,  34],
          ...,
          [ 58,  81,  96],
          [ 53,  76,  91],
          [ 58,  82,  94]],
  
         [[ 42,  46,  41],
          [ 39,  43,  38],
          [ 31,  35,  30],
          ...,
          [ 56,  79,  94],
          [ 55,  78,  93],
          [ 52,  76,  88]],
  
         [[ 40,  43,  41],
          [ 38,  41,  39],
          [ 30,  33,  31],
          ...,
          [ 52,  75,  90],
          [ 58,  81,  96],
          [ 58,  82,  94]],
  
         ...,
  
         [[ 30,  31,  29],
          [ 32,  33,  31],
          [ 30,  31,  29],
          ...,
          [138, 132, 125],
          [122, 116, 109],
          [138, 130, 123]],
  
         [[ 27,  28,  26],
          [ 32,  33,  31],
          [ 32,  33,  31],
          ...,
          [107, 102,  93],
          [ 98,  90,  83],
          [118, 110, 103]],
  
         [[ 27,  28,  26],
          [ 30,  31,  29],
          [ 27,  28,  26],
          ...,
          [108, 101,  92],
          [ 91,  84,  75],
          [107, 100,  91]]], dtype=uint8),
  'metadata': [{'x': 32,
    'y': 116,
    'w': 367,
    'h': 367,
    'coord1': {32, 116},
    'coord2': {399, 483}}],
  'detected_image': array([[[ 35,  39,  34],
          [ 37,  41,  36],
          [ 35,  39,  34],
          ...,
          [ 58,  81,  96],
          [ 53,  76,  91],
          [ 58,  82,  94]],
  
         [[ 42,  46,  41],
          [ 39,  43,  38],
          [ 31,  35,  30],
          ...,
          [ 56,  79,  94],
          [ 55,  78,  93],
          [ 52,  76,  88]],
  
         [[ 40,  43,  41],
          [ 38,  41,  39],
          [ 30,  33,  31],
          ...,
          [ 52,  75,  90],
          [ 58,  81,  96],
          [ 58,  82,  94]],
  
         ...,
  
         [[ 30,  31,  29],
          [ 32,  33,  31],
          [ 30,  31,  29],
          ...,
          [138, 132, 125],
          [122, 116, 109],
          [138, 130, 123]],
  
         [[ 27,  28,  26],
          [ 32,  33,  31],
          [ 32,  33,  31],
          ...,
          [107, 102,  93],
          [ 98,  90,  83],
          [118, 110, 103]],
  
         [[ 27,  28,  26],
          [ 30,  31,  29],
          [ 27,  28,  26],
          ...,
          [108, 101,  92],
          [ 91,  84,  75],
          [107, 100,  91]]], dtype=uint8)}]

# Writing to pandas dataframe
img_df=pd.DataFrame(img_df_list)
img_df.shape

(1091, 3)

img_df.head()

	orig_img	metadata	detected_image
0	[[[93, 186, 201], [89, 182, 197], [82, 177, 19...	[{'x': 146, 'y': 139, 'w': 428, 'h': 428, 'coo...	[[[93, 186, 201], [89, 182, 197], [82, 177, 19...
1	[[[255, 255, 255], [255, 255, 255], [255, 255,...	[{'x': 80, 'y': 139, 'w': 435, 'h': 435, 'coor...	[[[255, 255, 255], [255, 255, 255], [255, 255,...
2	[[[35, 39, 34], [37, 41, 36], [35, 39, 34], [3...	[{'x': 32, 'y': 116, 'w': 367, 'h': 367, 'coor...	[[[35, 39, 34], [37, 41, 36], [35, 39, 34], [3...
3	[[[142, 137, 158], [154, 150, 169], [163, 159,...	[{'x': 129, 'y': 166, 'w': 365, 'h': 365, 'coo...	[[[142, 137, 158], [154, 150, 169], [163, 159,...
4	[[[154, 159, 157], [153, 158, 156], [156, 161,...	[{'x': 47, 'y': 191, 'w': 352, 'h': 352, 'coor...	[[[154, 159, 157], [153, 158, 156], [156, 161,...

img_df['metadata'].head(2)

0    [{'x': 146, 'y': 139, 'w': 428, 'h': 428, 'coo...
1    [{'x': 80, 'y': 139, 'w': 435, 'h': 435, 'coor...
Name: metadata, dtype: object

# Function to show images from dataframe
def show_images_df(df,n):
    orig_img_list = df['orig_img'][:]
    detected_image_list = df['detected_image'][:]
    rand = np.random.randint(0, len(orig_img_list), n) # Generating n random numbers from total number of images
    print("Showing images with index in : {}".format(rand))
    plt.figure(figsize=(10, 20))
    for cnt,j in enumerate(rand):
        plt.subplot(n, 2, (cnt*2)+1)
        plt.imshow(orig_img_list[j])
        plt.subplot(n, 2, (cnt*2)+2)
        plt.imshow(detected_image_list[j])

show_images_df(img_df,5)

Showing images with index in : [ 566  638 1001 1068  203]

4. Save the output Dataframe in .csv format. [2 Marks]

# Saving dataframe to .csv file
img_df.to_csv('face_detection_out.csv',index=False)

# Checking if we are able to read the saved .csv file
read_df=pd.read_csv('face_detection_out.csv')
read_df.shape

(1091, 3)

PART C

1. Unzip, read and Load data(‘PINS.zip’) into session. [2 Marks]

from zipfile import ZipFile
zip_path='PINS.zip'
with ZipFile(zip_path, 'r') as z:
  z.extractall()

2. Write function to create metadata of the image. [4 Marks]

Hint: Metadata means derived information from the available data which can be useful for particular problem statement.

import numpy as np
import os

class IdentityMetadata():
    def __init__(self, base, name, file):
        # print(base, name, file)
        # dataset base directory
        self.base = base
        # identity name
        self.name = name
        # image file name
        self.file = file

    def __repr__(self):
        return self.image_path()

    def image_path(self):
        return os.path.join(self.base, self.name, self.file) 
    

def get_file_metadata(path, folder, file):
    # Check file extension. Allow only jpg/jpeg' files.
    ext = os.path.splitext(file)[1]
    if ext == '.jpg' or ext == '.jpeg':
        # getting path of every valid file 
        full_path=IdentityMetadata(path, folder, file)
    return full_path

# Checking if the function works 
get_file_metadata('PINS','pins_Aaron Paul','Aaron Paul0_262.jpg')

PINS\pins_Aaron Paul\Aaron Paul0_262.jpg

3. Write a loop to iterate through each and every image and create metadata for all the images. [4 Marks]

def load_metadata(path):
    metadata = []
    for folder in os.listdir(path):
        for file in os.listdir(os.path.join(path, folder)):
            full_path=get_file_metadata(path, folder, file)
            metadata.append(full_path)
    return np.array(metadata)

# Loading all images
pins_metadata = load_metadata('PINS')

pins_metadata.shape

(10770,)

# Checking a random image
pins_metadata[20]

PINS\pins_Aaron Paul\Aaron Paul140_243.jpg

pins_metadata[20].image_path()

'PINS\\pins_Aaron Paul\\Aaron Paul140_243.jpg'

4. Generate Embeddings vectors on the each face in the dataset. [4 Marks]

Hint: Use ‘vgg_face_weights.h5’

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation

def vgg_face():
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
    model.add(Convolution2D(64, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))
    
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))
    
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))
    
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))
    
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))
    
    model.add(Convolution2D(4096, (7, 7), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(4096, (1, 1), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(2622, (1, 1)))
    model.add(Flatten())
    model.add(Activation('softmax'))
    return model

vgg_face_model = vgg_face()

# Loading the weights given
vgg_face_model.load_weights('vgg_face_weights.h5')

print('Input of vgg_face_model  : {}'.format(vgg_face_model.inputs))
print('\nOutput of vgg_face_model : {}'.format(vgg_face_model.outputs))
print("---------------------------------")
print('Input of first layer     : {}'.format(vgg_face_model.layers[0].input))
print('\nOutput of first layer    : {}'.format(vgg_face_model.layers[0].output))
print("---------------------------------")
print('Input of last but previous layer : {}'.format(vgg_face_model.layers[-2].input))
print('\nOutput of last but previous layer: {}'.format(vgg_face_model.layers[-2].output))

Input of vgg_face_model  : [<KerasTensor: shape=(None, 224, 224, 3) dtype=float32 (created by layer 'zero_padding2d_input')>]

Output of vgg_face_model : [<KerasTensor: shape=(None, 2622) dtype=float32 (created by layer 'activation')>]
---------------------------------
Input of first layer     : KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='zero_padding2d_input'), name='zero_padding2d_input', description="created by layer 'zero_padding2d_input'")

Output of first layer    : KerasTensor(type_spec=TensorSpec(shape=(None, 226, 226, 3), dtype=tf.float32, name=None), name='zero_padding2d/Pad:0', description="created by layer 'zero_padding2d'")
---------------------------------
Input of last but previous layer : KerasTensor(type_spec=TensorSpec(shape=(None, 1, 1, 2622), dtype=tf.float32, name=None), name='conv2d_16/BiasAdd:0', description="created by layer 'conv2d_16'")

Output of last but previous layer: KerasTensor(type_spec=TensorSpec(shape=(None, 2622), dtype=tf.float32, name=None), name='flatten/Reshape:0', description="created by layer 'flatten'")

vgg_face_descriptor = Model(inputs=vgg_face_model.layers[0].input, outputs=vgg_face_model.layers[-2].output)

vgg_face_descriptor.inputs, vgg_face_descriptor.outputs

([<KerasTensor: shape=(None, 224, 224, 3) dtype=float32 (created by layer 'zero_padding2d_input')>],
 [<KerasTensor: shape=(None, 2622) dtype=float32 (created by layer 'flatten')>])

# Define function to load the image, reverse the image to RGB, normalize and resize 
def load_normalize_image(path):
    img = cv2.imread(path, 1)
    
    # reverse to RGB
    img_upd =img[...,::-1]
    
    # Normalising pixel values
    img_upd = (img_upd / 255.).astype(np.float32)
    
    #resizing to (224,224)
    img_upd = cv2.resize(img_upd, dsize = (224,224))
    
    return img_upd

# Test if the function works for a sample image
load_normalize_image(pins_metadata[0].image_path())

array([[[0.91207695, 0.7356064 , 0.6454103 ],
        [0.88488847, 0.7143265 , 0.62609994],
        [0.8638735 , 0.6964493 , 0.6183299 ],
        ...,
        [0.4222514 , 0.24705882, 0.27578783],
        [0.41568628, 0.24705882, 0.28235295],
        [0.41568628, 0.24705882, 0.28235295]],

       [[0.90540075, 0.73095226, 0.6367121 ],
        [0.86887133, 0.69931585, 0.60704523],
        [0.83423114, 0.66952527, 0.5825918 ],
        ...,
        [0.4222514 , 0.24705882, 0.27578783],
        [0.41568628, 0.24705882, 0.28235295],
        [0.41568628, 0.24705882, 0.28235295]],

       [[0.8978419 , 0.72529286, 0.62725365],
        [0.85586554, 0.68387043, 0.5861082 ],
        [0.80574644, 0.6399422 , 0.5461987 ],
        ...,
        [0.4188813 , 0.24368873, 0.27241772],
        [0.41231617, 0.24368873, 0.27898285],
        [0.4151221 , 0.24649464, 0.28178877]],

       ...,

       [[0.76963806, 0.46629983, 0.36387992],
        [0.76523983, 0.46524858, 0.3672269 ],
        [0.7585149 , 0.46375817, 0.3729231 ],
        ...,
        [0.30588236, 0.25882354, 0.29803923],
        [0.30588236, 0.25882354, 0.29803923],
        [0.30588236, 0.25882354, 0.29803923]],

       [[0.7799311 , 0.46722114, 0.36167732],
        [0.77430844, 0.4705267 , 0.36965147],
        [0.7704044 , 0.47374827, 0.38070413],
        ...,
        [0.30588236, 0.25882354, 0.29803923],
        [0.30588236, 0.25882354, 0.29803923],
        [0.30588236, 0.25882354, 0.29803923]],

       [[0.7875613 , 0.4738358 , 0.36795345],
        [0.7836222 , 0.47580534, 0.3718925 ],
        [0.77971816, 0.48103994, 0.3856443 ],
        ...,
        [0.30588236, 0.25882354, 0.29803923],
        [0.30588236, 0.25882354, 0.29803923],
        [0.30588236, 0.25882354, 0.29803923]]], dtype=float32)

# Get the embedding vector for this sample image
samp_img = load_normalize_image(pins_metadata[0].image_path())
embedding_vector = vgg_face_descriptor.predict(np.expand_dims(samp_img, axis=0))[0]
print("Shape of the embedding vector : {}".format(embedding_vector.shape))

1/1 [==============================] - 2s 2s/step
Shape of the embedding vector : (2622,)

## Let us check the sample values of embedded vectors generated for the sample image
for i in range(5):
    print(i, embedding_vector[i], type(embedding_vector[i]))

0 0.03170304 <class 'numpy.float32'>
1 -0.015051306 <class 'numpy.float32'>
2 -0.012434014 <class 'numpy.float32'>
3 0.01952115 <class 'numpy.float32'>
4 -0.00044385204 <class 'numpy.float32'>

## function to loop through all images and get the embedding vectors for all images
def get_all_embeddings(metadata):
    embeddings = np.zeros((metadata.shape[0], 2622)) # initializing for all 10770 images
    for i, m in enumerate(metadata):
        img_upd = load_normalize_image(metadata[i].image_path())
        embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img_upd, axis=0))[0]
        embeddings[i]=embedding_vector
    return embeddings 

## calling the function for pins_metadata
all_embeddings=get_all_embeddings(pins_metadata)
print("Shape of all_embedding : {}".format(all_embeddings.shape))

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Shape of all_embedding : (10770, 2622)

## Let us check the sample values of embedded vectors generated for the sample image
for i in range(3):
    for j in range(5):
        print(i, all_embeddings[i].shape, all_embeddings[i][j])

0 (2622,) 0.03170304000377655
0 (2622,) -0.01505130622535944
0 (2622,) -0.012434014119207859
0 (2622,) 0.01952115073800087
0 (2622,) -0.0004438520409166813
1 (2622,) 0.034977030009031296
1 (2622,) -0.001050618477165699
1 (2622,) -0.012489347718656063
1 (2622,) 0.025949543341994286
1 (2622,) 0.025733577087521553
2 (2622,) 0.028018906712532043
2 (2622,) -0.0011262879706919193
2 (2622,) -0.012657475657761097
2 (2622,) 0.029612626880407333
2 (2622,) 0.03246227651834488

5. Build distance metrics for identifying the distance between two similar and dissimilar images. [4 Marks]

# Function to caluclate the distance between 2 embeddings
def distance(emb1, emb2):
    return np.sum(np.square(emb1 - emb2))

# FUnction to show distance metrics between 2 given images
def show_pair(idx1, idx2):
    plt.figure(figsize=(15,5))
    path1=pins_metadata[idx1].image_path()
    path2=pins_metadata[idx2].image_path()
    dist=distance(all_embeddings[idx1], all_embeddings[idx2])
    plt.suptitle("Distance between images {0} & {1} = {2} \n".format(idx1, idx2, dist))

    ax1 = plt.subplot(1,2,1)
    ax1.set_title(path1)
    ax1.imshow(load_normalize_image(pins_metadata[idx1].image_path()))
    ax2 = plt.subplot(1,2,2)
    ax2.set_title(path2)
    ax2.imshow(load_normalize_image(pins_metadata[idx2].image_path()))
    

## Example of similar images
show_pair(20, 25)
show_pair(90, 97)
show_pair(450,460)
show_pair(630,640)

As shown above, the pair of images belong to same person and distance calculated is closer to 0

## Example of dissimilar images
show_pair(20, 95)
show_pair(90, 300)
show_pair(450,660)
show_pair(630,920)

As shown above, the pair of images belong to different person and distance calculated is closer to 1

6. Use PCA for dimensionality reduction. [2 Marks]

X=all_embeddings
y_arr=np.array([m.name for m in pins_metadata])
print("Shape of X :{}".format(X.shape))
print("Shape of y_arr :{}".format(y_arr.shape))

Shape of X :(10770, 2622)
Shape of y_arr :(10770,)

X

array([[ 0.03170304, -0.01505131, -0.01243401, ...,  0.0004314 ,
         0.00219081, -0.00908097],
       [ 0.03497703, -0.00105062, -0.01248935, ..., -0.01053091,
         0.00179321,  0.02439156],
       [ 0.02801891, -0.00112629, -0.01265748, ..., -0.00904751,
        -0.00578401,  0.02125831],
       ...,
       [ 0.00965871,  0.0037216 , -0.01345864, ..., -0.01433404,
         0.01899944,  0.03899154],
       [ 0.00230929,  0.00094763, -0.00544813, ..., -0.01600494,
         0.01053975,  0.03683249],
       [ 0.01772301,  0.01064051,  0.01995193, ..., -0.0261843 ,
         0.03288927,  0.04882649]])

y_arr

array(['pins_Aaron Paul', 'pins_Aaron Paul', 'pins_Aaron Paul', ...,
       'pins_zendaya', 'pins_zendaya', 'pins_zendaya'], dtype='<U29')

labels = preprocessing.LabelEncoder()
y = labels.fit_transform(y_arr)
print(labels.classes_)

['pins_Aaron Paul' 'pins_Alvaro Morte' 'pins_Amanda Crew'
 'pins_Amaury Nolasco' 'pins_Anna Gunn' 'pins_Benedict Cumberbatch'
 'pins_Betsy Brandt' 'pins_Brenton Thwaites' 'pins_Brit Marling'
 'pins_Bryan Cranston' 'pins_Caity Lotz' 'pins_Cameron Monaghan'
 'pins_Chance Perdomo' 'pins_Chris Evans' 'pins_Chris Pratt'
 'pins_Cobie Smulders' 'pins_Danielle Panabaker' 'pins_Dave Franco'
 'pins_Dominic Purcell' 'pins_Dwayne Johnson' 'pins_Emilia Clarke'
 'pins_Emily Bett Rickards' 'pins_Emma Stone' 'pins_Gwyneth Paltrow'
 'pins_Henry Cavil' 'pins_Jason Momoa' 'pins_Jeremy Renner'
 'pins_Jesse Eisenberg' 'pins_Jim Parsons' 'pins_Jon Bernthal'
 'pins_Josh Radnor' 'pins_Kit Harington' 'pins_Krysten Ritter'
 'pins_Kumail Nanjiani' 'pins_Maisie Williams' 'pins_Mark Ruffalo'
 'pins_Martin Starr' 'pins_Melissa benoit' 'pins_Mike Colter'
 'pins_Morena Baccarin' 'pins_Morgan Freeman' 'pins_Natalie Portman'
 'pins_Neil Patrick Harris' 'pins_Paul Rudd' 'pins_Pedro Alonso'
 'pins_Peter Dinklage' 'pins_RJ Mitte' 'pins_Rami Melek'
 'pins_Robert Knepper' 'pins_Robin Taylor' 'pins_Ryan Reynolds'
 'pins_Sarah Wayne Callies' 'pins_Scarlett Johansson'
 'pins_Sebastian Stan' 'pins_Sophie Turner' 'pins_Stephen Amell'
 'pins_Sundar Pichai' 'pins_Thomas Middleditch' 'pins_Tom Cavanagh'
 'pins_Ursula Corbero' 'pins_Wentworth Miller' 'pins_Willa Holland'
 'pins_William Fichtner' 'pins_alexandra daddario'
 'pins_alycia debnam carey face' 'pins_amber heard face'
 'pins_anne hathaway' 'pins_barbara palvin face' 'pins_bellamy blake face'
 'pins_bill gates' 'pins_brie larson' 'pins_chadwick boseman face'
 'pins_david mazouz' 'pins_drake' 'pins_dua lipa face' 'pins_eliza taylor'
 'pins_elizabeth olsen face' 'pins_elon musk' 'pins_emma watson face'
 'pins_gal gadot face' 'pins_grant gustin face' 'pins_jason isaacs'
 'pins_jeff bezos' 'pins_kiernan shipka' 'pins_kristen stewart face'
 'pins_lindsey morgan face' 'pins_margot robbie face' 'pins_maria pedraza'
 'pins_mark zuckerberg' 'pins_miguel herran' 'pins_millie bobby brown'
 'pins_rihanna' 'pins_robert downey jr face' 'pins_sean pertwee'
 'pins_selena gomez' 'pins_shakira' 'pins_tati gabrielle'
 'pins_taylor swift' 'pins_tom holland face' 'pins_zendaya']

y.shape

(10770,)

y

array([ 0,  0,  0, ..., 99, 99, 99], dtype=int64)

# Splitting the data into train and test
from sklearn.model_selection import train_test_split

#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=10, stratify=y)

train_idx = np.array([n for n in np.arange(pins_metadata.shape[0]) if n % 9 != 0])
test_idx = np.array([n for n in np.arange(pins_metadata.shape[0]) if n % 9 == 0])
print("Indexes in train_idx :{}".format(train_idx))
print("Indexes in test_idx  :{}".format(test_idx))


X_train = all_embeddings[train_idx]
X_test = all_embeddings[test_idx]
y_train = y[train_idx]
y_test = y[test_idx]

Indexes in train_idx :[    1     2     3 ... 10767 10768 10769]
Indexes in test_idx  :[    0     9    18 ... 10746 10755 10764]

print("Shape of X_train :{}".format(X_train.shape))
print("Shape of X_test :{}".format(X_test.shape))
print("Shape of y_train :{}".format(y_train.shape))
print("Shape of y_test :{}".format(y_test.shape))

Shape of X_train :(9573, 2622)
Shape of X_test :(1197, 2622)
Shape of y_train :(9573,)
Shape of y_test :(1197,)

scaler = StandardScaler()
X_train_std = scaler.fit_transform(X_train)
X_test_std = scaler.transform(X_test)

print('Shape of X_train_std shape : {}'.format(X_train_std.shape))
print('Shape of X_test_std shape  : {}'.format(X_test_std.shape))
print("Shape of y_train :{}".format(y_train.shape))
print("Shape of y_test :{}".format(y_test.shape))

Shape of X_train_std shape : (9573, 2622)
Shape of X_test_std shape  : (1197, 2622)
Shape of y_train :(9573,)
Shape of y_test :(1197,)

# Applying PCA
pca = PCA(n_components=224)
X_train_pca = pca.fit_transform(X_train_std)
X_test_pca = pca.transform(X_test_std)

print("After applying PCA for dimensionality reduction..")
print("Shape of X_train_pca shape : {}".format(X_train_pca.shape))
print("Shape of X_test_pca shape  : {}".format(X_test_pca.shape))
print("Shape of y_train :{}".format(y_train.shape))
print("Shape of y_test :{}".format(y_test.shape))

After applying PCA for dimensionality reduction..
Shape of X_train_pca shape : (9573, 224)
Shape of X_test_pca shape  : (1197, 224)
Shape of y_train :(9573,)
Shape of y_test :(1197,)

7. Build an SVM classifier in order to map each image to its right person. [4 Marks]

clf = SVC(C=5., gamma=0.001)
clf.fit(X_train_pca, y_train)

SVC(C=5.0, gamma=0.001)

predicted_labels_train = clf.predict(X_train_pca)

predicted_labels_test = clf.predict(X_test_pca)

def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x]==predictions[x]:
            correct += 1
    return (correct/float(len(testSet))) * 100.0

train_accuracy=getAccuracy(y_train.tolist(),predicted_labels_train)
print("Train score accuracy using SVM: {}".format(train_accuracy))

Train score accuracy using SVM: 99.97910790765695

test_accuracy=getAccuracy(y_test.tolist(),predicted_labels_test)
print("Test score accuracy using SVM: {}".format(test_accuracy))

Test score accuracy using SVM: 94.56975772765246

#sample actual Vs Predicted
y_grid = (np.column_stack([y_test, predicted_labels_test]))
print(y_grid[0:20])
print(y_grid[50:55])
print(y_grid[1000:1005])

[[ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [ 0  0]
 [63 63]
 [63 63]
 [63 63]
 [63 61]
 [63 63]
 [63 63]
 [63 63]
 [63 63]
 [63 63]
 [63 63]]
[[64 64]
 [64 64]
 [64 64]
 [64 64]
 [ 2  2]]
[[52 52]
 [52 52]
 [52 52]
 [52 52]
 [52 52]]

As we can see above the test classes and predicted classes are same for the samples.

y_predict_encoded = labels.inverse_transform(predicted_labels_test)

print(y_predict_encoded)

['pins_Aaron Paul' 'pins_Aaron Paul' 'pins_Aaron Paul' ... 'pins_zendaya'
 'pins_zendaya' 'pins_zendaya']

class_dict={i: l for (i, l) in enumerate(labels.classes_)}
print('\nClass dictionary   : \n{}'.format(class_dict))

Class dictionary   : 
{0: 'pins_Aaron Paul', 1: 'pins_Alvaro Morte', 2: 'pins_Amanda Crew', 3: 'pins_Amaury Nolasco', 4: 'pins_Anna Gunn', 5: 'pins_Benedict Cumberbatch', 6: 'pins_Betsy Brandt', 7: 'pins_Brenton Thwaites', 8: 'pins_Brit Marling', 9: 'pins_Bryan Cranston', 10: 'pins_Caity Lotz', 11: 'pins_Cameron Monaghan', 12: 'pins_Chance Perdomo', 13: 'pins_Chris Evans', 14: 'pins_Chris Pratt', 15: 'pins_Cobie Smulders', 16: 'pins_Danielle Panabaker', 17: 'pins_Dave Franco', 18: 'pins_Dominic Purcell', 19: 'pins_Dwayne Johnson', 20: 'pins_Emilia Clarke', 21: 'pins_Emily Bett Rickards', 22: 'pins_Emma Stone', 23: 'pins_Gwyneth Paltrow', 24: 'pins_Henry Cavil', 25: 'pins_Jason Momoa', 26: 'pins_Jeremy Renner', 27: 'pins_Jesse Eisenberg', 28: 'pins_Jim Parsons', 29: 'pins_Jon Bernthal', 30: 'pins_Josh Radnor', 31: 'pins_Kit Harington', 32: 'pins_Krysten Ritter', 33: 'pins_Kumail Nanjiani', 34: 'pins_Maisie Williams', 35: 'pins_Mark Ruffalo', 36: 'pins_Martin Starr', 37: 'pins_Melissa benoit', 38: 'pins_Mike Colter', 39: 'pins_Morena Baccarin', 40: 'pins_Morgan Freeman', 41: 'pins_Natalie Portman', 42: 'pins_Neil Patrick Harris', 43: 'pins_Paul Rudd', 44: 'pins_Pedro Alonso', 45: 'pins_Peter Dinklage', 46: 'pins_RJ Mitte', 47: 'pins_Rami Melek', 48: 'pins_Robert Knepper', 49: 'pins_Robin Taylor', 50: 'pins_Ryan Reynolds', 51: 'pins_Sarah Wayne Callies', 52: 'pins_Scarlett Johansson', 53: 'pins_Sebastian Stan', 54: 'pins_Sophie Turner', 55: 'pins_Stephen Amell', 56: 'pins_Sundar Pichai', 57: 'pins_Thomas Middleditch', 58: 'pins_Tom Cavanagh', 59: 'pins_Ursula Corbero', 60: 'pins_Wentworth Miller', 61: 'pins_Willa Holland', 62: 'pins_William Fichtner', 63: 'pins_alexandra daddario', 64: 'pins_alycia debnam carey face', 65: 'pins_amber heard face', 66: 'pins_anne hathaway', 67: 'pins_barbara palvin face', 68: 'pins_bellamy blake face', 69: 'pins_bill gates', 70: 'pins_brie larson', 71: 'pins_chadwick boseman face', 72: 'pins_david mazouz', 73: 'pins_drake', 74: 'pins_dua lipa face', 75: 'pins_eliza taylor', 76: 'pins_elizabeth olsen face', 77: 'pins_elon musk', 78: 'pins_emma watson face', 79: 'pins_gal gadot face', 80: 'pins_grant gustin face', 81: 'pins_jason isaacs', 82: 'pins_jeff bezos', 83: 'pins_kiernan shipka', 84: 'pins_kristen stewart face', 85: 'pins_lindsey morgan face', 86: 'pins_margot robbie face', 87: 'pins_maria pedraza', 88: 'pins_mark zuckerberg', 89: 'pins_miguel herran', 90: 'pins_millie bobby brown', 91: 'pins_rihanna', 92: 'pins_robert downey jr face', 93: 'pins_sean pertwee', 94: 'pins_selena gomez', 95: 'pins_shakira', 96: 'pins_tati gabrielle', 97: 'pins_taylor swift', 98: 'pins_tom holland face', 99: 'pins_zendaya'}

samp_idx = 70
print("Actual index of the image :{}".format(test_idx[samp_idx]))

samp_img = load_normalize_image(pins_metadata[test_idx][samp_idx].image_path())
samp_pred = predicted_labels_test[samp_idx]
print("Predicted class :{}".format(samp_pred))

samp_id =  class_dict[samp_pred]

plt.imshow(samp_img)
plt.title("Sample image below is identified as {}".format(samp_id))

Actual index of the image :630
Predicted class :3

Text(0.5, 1.0, 'Sample image below is identified as pins_Amaury Nolasco')

8. Import and display the the test images. [2 Marks]

Hint: ‘Benedict Cumberbatch9.jpg’ and ‘Dwayne Johnson4.jpg’ are the test images.

benedict_pic='Benedict Cumberbatch9.jpg'
benedict_img = load_normalize_image(benedict_pic)
plt.imshow(benedict_img)

<matplotlib.image.AxesImage at 0x29c34075a90>

dwayne_pic='Dwayne Johnson4.jpg'
dwayne_img = load_normalize_image(dwayne_pic)
plt.imshow(dwayne_img)

<matplotlib.image.AxesImage at 0x29b0b342fd0>

9. Use the trained SVM model to predict the face on both test images. [4 Marks

def predict_sample(path):
    # reading the image
    samp_img = load_normalize_image(path)
    
    #Preparing embedded vector and model input 
    embedding_vector = vgg_face_descriptor.predict(np.expand_dims(samp_img, axis=0))[0]
    samp_embeddings = np.zeros((1, 2622)) # initializing for the image
    samp_embeddings[0]=embedding_vector
    x_samp=samp_embeddings
    
    # standardize
    x_samp_std = scaler.transform(x_samp)
    
    # apply pca
    x_samp_pca = pca.transform(x_samp_std) 
    
    # predict using the trained model 
    predicted_class = clf.predict(x_samp_pca) 
    samp_pred=predicted_class[0]
    print("predicted_class : {}".format(samp_pred))
    
    
    # display actual image with predicted name
    plt.imshow(samp_img)
    plt.title("Image below is identified as {}".format(class_dict[samp_pred]))

predict_sample(benedict_pic)

1/1 [==============================] - 2s 2s/step
predicted_class : 5

predict_sample(dwayne_pic)

1/1 [==============================] - 0s 373ms/step
predicted_class : 19

The model has correctly predicted the names of the actors present in the 2 given images

THE END